Offshore wind energy, characterized by resource potential and sustainable attributes, holds strategic importance in the global energy transition. Despite its advantages of high energy density, stable wind regimes, and reduced land-use conflicts, current exploitation levels remain limited. This is due to the difficulty of offshore units and the volatility and intermittency of wind energy, which lead to non-stationary and multi-scale fluctuations in wind power signals, posing significant challenges for accurate forecasting. To address this, we propose strengthening Hankel-DMD (Hankel Dynamic Mode Decomposition) by incorporating an improved discrete-time coefficient weighting (DTW) criterion, which retains physically interpretable modes to enhance analyzability. Furthermore, to compensate for nonlinear dynamics overlooked during mode decomposition, a Long Short-Term Memory (LSTM) network is integrated into the framework, establishing a physics-data hybrid model that synergizes linear dynamics identification with data-driven residual correction to enhance accuracy. We present a hybrid forecasting framework that combines domain knowledge with data-driven models to provide both improved accuracy and physical interpretability. Applied to real offshore wind data, the method attains a post-compensation R2 = 0.937 and lowers overall forecasting error below 8%, yielding engineering-grade short-term forecasts suitable for grid operation and planning.
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